Medical image segmentation is significant for the accurate screening of diseases and the prognosis of patients. The evaluation of lesions based on lesion segmentation provides the information about disease progression and help physicians improve the quality of clinical diagnosis and treatment. However, manual lesion segmentation is rather subjective and laborious, which limits its potential clinical application. Recently, with the fast development of artificial intelligence, algorithms based on deep learning have been widely used and have achieved the state-of-the-art performance in medical image segmentation [1]}. Convolutional neural networks (CNNs) are popular for lesion segmentation in medical images owing to its high segmentation quality. Such algorithms typically feature a deep encoder to extract features automatically from the input images and the following operations to generate dense predictions. For example, Long et al. [2]} proposed a fully convolutional network for image semantic segmentation, which is rather influential and inspires the later end-to-end frameworks in medical segmentation. Ronneberger et al. [3]} proposed a U-shaped network (U-Net) for medical image segmentation, which has shown promising results in many fields of medical segmentation and has become a virtual benchmark in many medical segmentation tasks.
